This research describes a hands-off socially assistive therapist robot
that monitors, assists, encourages, and socially interacts with post-stroke
users engaged in rehabilitation exercises. We investigate the role of the robot's
personality in the hands-off therapy process, focusing on the relationship between
the level of extroversion-introversion of the robot and the user. We also
demonstrate a behavior adaptation system capable of adjusting its social interaction
parameters (e.g., interaction distances/proxemics, speed, and vocal
content) toward customized post-stroke rehabilitation therapy based on the
user's personality traits and task performance.

Achieving a psychological "common ground" between the human user
and the robot is necessary for a natural, nuanced, and engaging interaction.
Therefore, in this work we investigated the role of the robot's personality in
the assistive therapy process. We focus on the relationship between the level
of extroversion-introversion (as defined in the Eysenck Model of Personality) of the robot and the user, addressing the following research questions:
1) Is there a relationship between the extroversion-introversion personality spectrum (assessed with the Eysenck model) and the challenging vs. nurturing style of patient encouragement?
2) How should the behavior and encouragement of the therapist robot adapt as a function of the user's personality and task performance?

In our interaction design, we chose to use the following traits: 1) sociability and 2) activity.
These traits can be most readily emulated in robot behavior. We expressed
those traits through three main parameters that define the therapist robot
behavior: 1) interaction distance / proxemics, 2) speed, and 3) verbal and
para-verbal communication.

The main goal of our robot behavior adaptation system is to enable us to optimize
on the fly the three main parameters (interaction distance/proxemics,
speed, and vocal content) that define the behavior (and thus personality)
of the therapist robot, so as to adapt it to the user's personality and thus
improve the user's task performance. Task performance is measured as the
number of exercises performed in a given period of time; the learning system
changes the robot's personality, expressed through the robot's behavior, in
an attempt to maximize the task performance metric.
We formulated the problem as policy gradient reinforcement learning
(PGRL) and developed a learning algorithm that consists of the following
steps: (a) parametrization of the behavior; (b) approximation of the gradient
of the reward function in the parameter space; and (c) movement towards
a local optimum.

Personality-matching experimental results: The experimental studies validated our two hypotheses. The
participants with extroverted personalities had a preference for a robot that
challenged them during exercises over the one that focused the interaction
on praise. Analogously, users with introverted personalities preferred the
robot that focuses on nurturing praise rather than on challenge-based motivation
during the training program. Thus, the results show user preference
for human-robot personality matching in the socially assistive context.

Robot behavior adaptation experimental results: The experimental results provide first
evidence for the effectiveness of robot behavior adaptation to user personality and
performance.